Abstract

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Markov confusion matrices, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 84,616 minutes of recorded free-living behaviours from 57 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,609 participants. For example, we find that men spend more time in both low- and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

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